In recent years, ‘perovskite solar cells’ have been the subject of intense research because they are low-cost replacement to the conventional silicon-based photovoltaic cells. However, perovskites are not very stable — otherwise they would be ruling the solar market by now. 

‘Perovskites’ refer to any material that is of a certain (ortho-rhombic) crystal structure similar to that of calcium titanate, which is a naturally occuring mineral (discovered in 1939 by Russian mineralogist Gustav Rose, who named it after his friend Lev Perovski). 

Now, researchers at IIT-Guwahati have addressed the stability issue through a method which involves passivation by organic additives. In simpler terms, it means coating the perovskite cell with an ultra-thin (therefore two-dimensional) layer of chemicals (polymers and conjugated poly-electrolytes). 

“Among all the approaches, surface passivation is one of the most convenient techniques to enhance the stability and efficiency of PSCs, where an ultra-thin layer is precisely formed over three-dimensional (3D) light harvesting perovskite materials that effectively reduces the charge recombination and enhances the carrier transport as well as stability,” says a paper in The Journal of Materials Chemistry, published by the IIT-Guwahati research team led by Parameswar Krishnan Iyer. 

When the perovskite layer is treated with a larger organic ammonium halide salt, a thin 2D layer is formed over the 3D structure. This 2D-3D perovskite offers enhanced charge transport and higher generation. Efficiencies of 21.18 per cent have been observed in experiments. The 2D layer improves stability under ambient conditions due to its improved hydrophobicity (water-repelling nature).

X-ray for Covid detection 

IIT-Jodhpur researchers have developed an explainable artificial intelligence (XAI) solution for predicting Covid-19 from chest X-rays.

XAI is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. XAI is used to describe an AI model, its expected impact and potential biases. AI explainability also helps an organisation adopt a responsible approach to AI development. 

The researchers at IIT-Jodhpur have developed a deep-learning-based algorithm called COMiT-Net, which learns the abnormalities present in chest X-ray images to identify a Covid-19-affected lung. The developed algorithm not only predicts Covid-19 pneumonia but can also identify the infected regions in the lungs, thus making them explainable. 

While there has been much research on Covid-19 detection using X-ray or CT scans in the past year, most fail to provide an explainable solution.

The proposed study can visually showcase the infected region. The technique interprets only from the lung region. The AI solution used in this research is explainable from both algorithmic and medical points of view. 

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